As the leading astrobiology university student society, run by students, for students, at the University of Manchester Astrobiology Society we are embarked on a mission to spread the word about astrobiology, enrich the university community by delivering high-quality events and resources, and create a global network of students and young professionals that will become the astrobiologists of tomorrow. Ever since our foundation, we have gone beyond the classic student society activities by inviting world-renowned researchers, creating a dedicated careers program, or even organizing the first student-led online congress at the university. In addition, our innovation team always ensures that we use the latest tools and technologies to keep thriving in our ever-evolving world. In this sense, we have developed an all-in-one website where people interested in astrobiology can discover, learn and connect. Furthermore, we have also built a global community using social media, with almost 3000 followers spread over 17 countries. By leading by example, we want to spark change among student societies and for them to redefine their boundaries and to achieve bigger. At AbSciCon 2022 we aim to further pursue this goal, grow our network, share expertise, and learn from others.

The Scintillation Observations and Response of The Ionosphere to Electrodynamics (SORTIE) mission is a 6U CubeSat that has been making ionospheric measurements at 420 km altitude since February 19, 2020. The SORTIE sensor suite includes an Ion Velocity Meter (IVM), which is used in the present study to detect and characterize Traveling Ionospheric Disturbances (TIDs). On July 11, 2020 the SORTIE orbit passed over near-concentric TIDs that were seen in the Total Electron Content (TEC) data from ground-based Global Positioning System receivers distributed across the COntiguous United States (CONUS). The TID wave characteristics estimated from the IVM data agree well with those determined from the ground-based TEC data. The wave periods derived from the SORTIE data are shorter than the TID periods in the TEC data but are anticipated and explained in terms of the classical Doppler effect. A numerical simulation was performed using the Weather Research and Forecasting (WRF) model that shows excitation of atmospheric gravity waves (AGWs) from a deep convective storm over Texas preceding TID observations by SORTIE. We show that these AGWs were observed at stratospheric heights in close proximity to the convective storm by the Atmospheric Infrared Sounder onboard the NASA Aqua satellite, and in the lowermost mesosphere by the Cloud Imaging and Particle Size instrument onboard the NASA Aeronomy of Ice in the Mesosphere satellite. These storm-generated AGWs, or the associated higher-order AGWs, are the likely sources of the TIDs observed in the ground-based TEC and SORTIE IVM data.

Hurricane Irma affected the Florida peninsula in September 2017. The east coast of the peninsula was hit particularly hard: the city of Jacksonville flooded around the St. Johns River estuary with non-tidal water levels that exceeded 1.5 m and precipitation that surpassed 20 cm in 24 hours. This study used observations such as water and wind velocities, river discharge, and conductivity data to determine whether compounding forcings influenced flood levels. Results show that flooding was initiated by a pulse from the ocean and then exacerbated by high river discharge. The 1-2 punch from the ocean and then the river caused record flooding, with impacts that lasted through the rest of September. Peak water levels occurred while hurricane winds were receding, and river discharge was increasing. Compound flood models should consider the phase lag between driving processes, as the individual peaks may not occur simultaneously, yet exacerbate flooding.

The knowledge of the strain/stress field evolution in time is important to seismic hazard assessment and risk mitigation, and is fundamental to the understanding of the earth dynamic system. Based on the principle that past tectonic stress should have left traces in the rocks, geologists have been trying to determine the paleostress history from evidence found in rocks for decades. Recent development of techniques for automatic extraction of fracture surfaces from digital outcrop models and estimation of historical shear deformation on rock fractures provide an efficient way of quantitatively acquiring large amount of high quality fracture/fault slip data (direction and sense of slip occurs on the fault plane) from outcrops. So unlike traditional paleostress inversion methods whose data is manually collected in the field, this high quality fracture/fault slip data provide an opportunity to develop fully automatic and quantitative methods for deciphering paleostrain. In this study, for slip on each fracture, the corresponding local strain tensor is calculated, then the local strain tensors are grouped into populations corresponding to far-field strain events and local strain events using a clustering analysis technique. The applications on outcrops in the eastern Tian Shan area give a clear picture of the paleostrain variation over space and time, and also throw light on the relationship between paleostrain, fracture development and the distribution of shear displacements in a thrusting environment.

Modeling the shortwave radiation balance over the Southern Ocean region remains a challenge for Earth system models. To investigate whether this is related to the representation of aerosol-cloud interactions, we compared measurements of the total number concentration of sea spray generated particles within the Southern Ocean region to model predictions thereof. Measurements were conducted from a container laboratory aboard the R/V Tangaroa throughout an austral summer voyage to the Ross Sea. We used source-receptor modeling to calculate the sensitivity of our measurements to upwind surface fluxes. From this approach, we could constrain empirical parameterizations of sea spray surface flux based on surface wind speed and sea surface temperature. A newly tuned parameterization for the flux of sea spray particles based on the near-surface wind speed is presented. Comparisons to existing model parameterizations revealed that present model parameterizations led to over-estimations of sea spray concentrations. In contrast to previous studies, we found that including sea surface temperature as an explanatory variable did not substantially improve model-measurement agreement. To test whether or not the parameterization may be applicable globally, we conducted a similar regression analysis using a database of in situ whitecap measurements. We found that the key fitting parameter within this regression agreed well the parameterization of sea spray flux. Finally, we compared calculations from the best model of surface flux to boundary layer measurements collected onboard an aircraft throughout the Southern Ocean Clouds, Radiation, Aerosol Transport Experimental Study (SOCRATES), finding good agreement overall.

In organic soils the availability of electron acceptors determines the ratio of CO2 to CH4 formation under anoxic conditions. While typically only inorganic electron acceptors are considered, the importance of electron accepting capacities of organic matter is increasingly acknowledged. Redox properties of organic matter are yet only investigated for a limited set of peat and reference materials. Therefore, we incubated 60 peat samples of 15 sites located in five major peatland regions covering a variety of both bog and fen samples and characterized their capacities to serve as electron acceptor for anaerobic CO2 production. We quantified CO2 and CH4 formation, and changes in available EAC in anoxic incubations of 56 days. On the time scale of our experiment, on average 36.5 % of CO2 could be attributed to CH4 formation, assuming an CO2/CH4 ratio for methanogenesis of 1:1. Regarding the remaining CO2 formed, for which a corresponding electron acceptor would be needed, we could on average explain 70.8 % by corresponding consumption of EAC from both organic and inorganic electron acceptors, the latter contributing typically less than 0.1 %. When the initial EAC was high, CO2 formation from apparent consumption of EAC was high and outweighed CO2 formation from methanogenesis. A rapid depletion of available EAC resulted in a higher share of CO2 from CH4 formation. Our study demonstrates that EAC provides the most important redox buffer for competitive suppression of CH4 formation in peat soils. Moreover, electron budgets including EAC of organic matter could largely explain anaerobic CO2 production.

The scientific community is becoming more demographically diverse, and team science is becoming more common. Here, we compare metrics of success in STEM, such as acceptance rates and citations, between differing team compositions regarding nationality, gender, career stage, and race/ethnicity. We collected the final decisions and citations as of 2019 of 91,427 manuscripts submitted from 2012-2018 to journals published by the American Geophysical Union. We matched the authors by email on each manuscript to self-provided demographic information within the American Geophysical Union’s membership database. This resulted in 20,940 manuscripts matched to nation, gender, and career stage, and 6,015 manuscripts matched to race/ethnicity for manuscripts whose entire authorship team was affiliated with the U.S. Among similar sized authorship teams (teams of 2-4), acceptance rates were 2.7, 4.5, and 0.9% higher (pnation < 0.01, pgender < 0.05, pcareer stage = 0.51) with more than one nation, gender, and career stage, respectively, than non-diverse authorship teams. Diverse papers had 1.2 more citations for international teams than single-nation teams (pnation < 0.01). There were 0.4 and 1.0 fewer citations for authorship teams with more than one gender or career stage than manuscripts with one gender or one career stage (pgender = 0.21, pcareer stage = 0.36). However, racially/ethnically diverse teams were associated with 5.5% lower acceptance rates (p < 0.01) and 0.8 fewer citations (p = 0.15) than racially/ethnically homogenous teams. These results show that diversity can have tangible benefits to science, but equitable practices and inclusive cultures must also be fostered.

The Henkel plot (logarithm of magnetic susceptibility versus density of rock samples) reveals that most rocks fall on either a “magnetite trend” or a “paramagnetic trend”. Interpretation of gravity and magnetic surveys is improved when the mineralogical and lithological basis of these trends is understood. We present a quantitative mineralogical mixing model, involving the components QFC (quartz-feldspar-calcite), FM (ferromagnesian silicates), and M (magnetite), and discuss the geological processes which produce or modify these mixtures. Igneous rocks mostly plot on the magnetite trend, where the FM/M ratio is about 10. The density-susceptibility mineralogical mixing model is compatible with the CIPW mineral calculation for igneous classification from chemical analyses. Sedimentary and metamorphic processes usually involve oxidation, reduction, and/or iron loss, all which are magnetite-destructive and lead to petrophysical measurements along the paramagnetic trend where FM/M >1000. Mineralization, with the introduction of sulfides and oxides leads to dense rocks which do not plot along the magnetite nor paramagnetic trends. This quantitative analysis provides a method to integrate geological processes in the interpretation of geophysical surveys.

Rivers are an important source of freshwater that support societal needs and natural ecosystems, functioning as both collectors for watersheds and distributors along river corridors. Human-made infrastructure (dams, roads, canals) of various kinds have been built on and along rivers to access drinking water, generate energy, mitigate floods, and support industrial and agricultural production. However, due to the long and inconsistent history of constructing and recording these structures, we lack a globally consistent knowledge about where different types of infrastructure are. Here, we used a simple yet consistent method to visually locate and classify different infrastructures that could act as obstructions on rivers that are wider than 30 meters (total length ~2.1 million km globally). Our approach is based on Google Maps’ high resolution satellite images, which for many places have meter-scale resolution. We recently completed global-scale mapping and classifying different obstructions, and are conducting quality checks. In total, we identified ≥ 40,000 unique obstructions, including large dams and smaller weirs, control structures, partial barriers, as well as low-head dams that are often not included in other databases. This Global River Obstruction Dataset, or GROD, once fully validated, will be freely available to the public. We anticipate that it will be of wide interest to hydrological modeling, aquatic ecosystem, geomorphology, and water resource management communities.

River-influenced areas are very important coastal ecosystems, due to their influence on nutrients and the structure of planktonic and benthic communities. Many studies have focused on the interaction between river runoff, wind and river plume characteristics, by means of observations and modeling. This study aimed to improve our understanding about the circulation and seasonal pattern of river plumes in Central Chile, using the Regional Ocean Modeling System (ROMS). Results were in reasonable agreement with observations. The plumes exhibit a minimal area during summer-fall, with a northwest orientation caused by southwesterly winds and lower river discharges. A larger plume area is developed during June-September, as result of higher winter precipitations. The orientation of the plumes during winter is westward, driven by higher river discharge and wind from the northwest. An interesting feature reproduced by the model was the trend to coalesce as observed in satellite imagery.

In Greek mythology, Cassandra was cursed with a prophetic knowledge of the future that no one would believe. The premise of this mythological prophet is also an apt description of the challenge facing scientists in communicating climate change. Central to addressing this challenge is the ability to examine, interpret and understand the individual scaffolding that underlies people's perception of climate change. Human-centered design incorporates a deep assessment of human needs in the design of systems and services, providing an alternative lens to engage in action towards climate change. Over the course of three separate design classes, attracting a diverse array of students from undergraduate to PhD to professional degrees, from engineers to humanists, we have explored the intersection of design education and climate change communication. In Design for a Habitable Planet, students conducted broad unstructured interviews to populate a matrix of beliefs and experiences around climate change, allowing them to bridge a perceived empathy gap. Their subjects included local fisherman, who are experiencing climate change but are ideologically opposed to the concept, and Alaska Native students who are experiencing climate change as part of a larger portfolio of challenges facing their communities. Leveraging theories from behavioral psychology, students then developed and prototyped an array of climate action campaigns, using an array of mediums from Snapchat filters to crowd-sourced climate haiku on beer labels. A unique outcome of the design process was a set of strategies intended to engage voices from all corners of the climate change debate and in turn inspire action. In Design of Data, students were challenged to design a data journalism piece highlighting positive action to address climate change. Students struggled with their pre-conceived negative bias around climate change, but after an examination of their own communities, what emerged were eight compelling stories daylighting positive and collective action to address global change. Ultimately, through our classroom experiments integrating design, behavioral psychology and climate change communication, students have not only learned to engage across differences, but we have developed a first glimpse of the power of design to inspire novel strategies to incite positive action to address climate change.

My laboratory is developing an accelerometer. However, if you do not install the dedicated software, you can not browse the data. In addition, since it is necessary to go to the site on a semi-annual basis and collect data due to capacity problems, the current operation is burdened. In order to solve these problems, we devised WebGL that enables high-speed drawing when processing a large amount of data such as Big Data, and a Web application that uses server that can transmit, save, and download acquired data. In this application, an accelerometer and a raspberry pie are connected, and the raspberry pie receives data obtained from the accelerometer and sends the data to a server installed in a laboratory by SCP file transfer at regular intervals. These data specify the location and date from the client side through the Internet browser, download the data using the download function of the browser, read this data on the application and draw the waveform. The data obtained from the accelerometer in the development to date is converted into a CSV file and transferred to the server. These data can be downloaded and displayed on the web application. For the google map displayed on the right side of the screen, it is to make the application more useful by registering the location where the accelerometer was set in the future.

The Infrared Atmospheric Sounder Interferometer (IASI) is well suited for monitoring of dust aerosols because of its capability to determine both AOD and altitude of the dust layer, and because of the good match between the IASI times of observation (9.30 am and pm, local time) and the time of occurrence of the main Saharan dust uplift mechanisms. Here, starting from IASI-derived dust characteristics for an 11-year period, we assess the capability of IASI to bring realistic information on the dust diurnal cycle. We first show the morning and nighttime climatology of IASI-derived dust AOD for two major dust source regions of the Sahara: The Bodele Depression and the Adrar region. Compared with simulations from a high resolution model, permitting deep convection to be explicitly resolved, IASI performs well. In a second step, a Dust Emission Index specific to IASI is constructed, combining simultaneous information on dust AOD and mean altitude, with the aim of observing the main dust emission areas, daytime and nighttime. Comparisons are then made with other equivalent existing results derived from ground based or other satellite observations. Results demonstrate the capability of IASI to improve the documentation of dust distribution over Sahara over a long period of time. Associating observations of dust aerosols in the visible, on which a majority of aerosol studies are so far based, and in the infrared thus appears as a way to complement the results from other satellite instruments in view of improving our knowledge of their impact on climate.

It was investigated that the reproducibility of surface wind and tracer transport simulations over complex terrain in wintertime using high-resolution (5-km, 3-km, and 1-km grid) weather and transport models, in which radioactive cesium (Cs-137) emitted from the Fukushima nuclear power plant was used as a tracer. Fukushima has complex terrain, such as mountains and valleys. The model results were validated by observations collected from the national networks of the automated meteorological data acquisition system and the hourly air pollution sampling system. The reproducibility depended on the model resolution, topographic complexity, and synoptic weather conditions. Higher model resolution led to higher reproducibility of surface winds, especially in mountainous areas when the Siberian winter monsoon was disturbed. In contrast, the model improvement was negligible or nonexistent over plain/coastal areas when the synoptic field was steady. The statistical scores of the tracer transport simulations often deteriorated due to small errors in the plume locations. However, the higher-resolution models advantageously performed better transport simulations in the mountainous areas because of the lower numerical diffusion and higher reproducibility of the mass flux. The reproducibility of the tracer distribution in the valley of the Fukushima mountainous region was dramatically improved with increasing model resolution. In conclusion, a higher-resolution model is definitely recommended for tracer transport simulations over mountainous terrain at least in the range of mesoscale model resolutions (commonly 1~10 km grids).

In Ceres dwarf planet, a portion of a prior ocean at shallow depth may still exist today as localized reservoirs. In this work, we constrained chemical and geophysical properties of initial aqueous fluids, characterizing the reservoirs, located under two selected craters: Kupalo and Juling. At first, we applied FREZCHEM code to simulate brines’ freezing processes, under different values of initial total pressure in which the starting solutions have cooled to precipitate the solids characterizing these craters. Then, we compared the results with our chemical equilibria calculations to understand the equilibrium state for each precipitated mineral, during the cooling process, related to the activities of solutes and the ionic strength of solutions. Decreasing temperature caused the precipitations of carbonates (thermodynamically favored), followed by the formation of sulphates and, later, of Cl-bearing salts from more saline brines. Solids precipitation feeds cooling process, changing the velocity/density ratios of aqueous solutions that would have arrived at surface erupting with a velocity of ⁓8·10-5 m/s. An excess of pressure in the reservoirs could have supported an intrusion of briny materials to surface, and, in our simulations, we suggest that sodium-salts formation is pressure-dependent. This supports the hypothesis that different “cooling chambers”, at different pressure conditions, may exist under the craters. Moreover, beneath Kupalo, at specific pressure condition, some kinetics-dependent molecules could form, suggesting that aqueous solutions plausibly were affected by kinetics slower than the nearby Juling.

Stochastic parameterizations are broadly used in climate modeling to represent subgrid scale processes. While different parameterizations are being developed considering different aspects of the physical phenomena, less attention is given to the technical and numerical aspects. In particular, the use of Empirical Orthogonal Functions (EOFs) is well established whenever a spatial structure is required, without considering its possible drawbacks. By applying an energy consistent parameterization to the 2-layer Quasi-Geostrophic (QG) model, we investigate the model sensitivity to the \emph{a priori} assumptions made on the parameterization. In particular, we consider here two methods to prescribe the spatial covariance of the noise. First, by using climatological variability patterns provided by EOFs, and second, by using time-varying dynamics-adapted Koopman modes, approximated by Dynamic Mode Decomposition (DMD). The performance of the two methods are analyzed through numerical simulations of the stochastic system on a coarse spatial resolution, and the outcomes compared to a high-resolution simulation of the original deterministic system. The comparison reveals that the DMD based noise covariance scheme outperforms the EOF based. The use of EOFs leads to a significant increase of the ensemble spread, and to a meridional misplacement of the bi-modal eddy kinetic energy (EKE) distribution. On the other hand, using DMDs, the ensemble spread is confined and the meridional propagation of the zonal jet stream is accurately captured. Our results highlight the importance of the systematic design of stochastic parameterizations with dynamically adapted spatial correlations, rather than relying on statistical spatial patterns.

We consider the statistical properties of solutions of the stochastic fractional relaxation equation that has been proposed as a model for the earth’s energy balance. In thisequation, the (scaling) fractional derivative term modelsenergy storage processes that occur over a wide range of space and time scales. Up until now, stochastic fractionalrelaxation processes have only been considered withRiemann-Liouville fractional derivatives in the context of random walk processes where it yields highlynonstationary behaviour. For our purposes we require the stationary processes that are the solutions of the Weyl fractional relaxation equations whose domain is −∞ to t rather than 0 to t. We develop a framework for handling fractional equationsdriven by white noise forcings. To avoid divergences, wefollow the approach used in fractional Brownian motion(fBm). The resulting fractional relaxation motions (fRm) and fractional relaxation noises (fRn) generalize the more familiar fBm and fGn (fractional Gaussian noise). Weanalytically determine both the small and large scale limitsand show extensive analytic and numerical results on the autocorrelation functions, Haar fluctuations and spectra. We display sample realizations. Finally, we discuss the prediction of fRn, fRm which – due to long memories - is a past value problem, not an initial value problem. We develop an analytic formula for the fRnforecast skill and compare it to fGn. Although the large scale limit is an (unpredictable) white noise that is attainedin a slow power law manner, when the temporal resolutionof the series is small compared to the relaxation time, fRncan mimick a long memory process with a wide range of exponents ranging from fGn to fBm and beyond. Wediscuss the implications for monthly, seasonal, annualforecasts of the earth’s temperature.

Low-cost air quality monitors (LCAQMs) are promising supplements to regulatory monitors for PM2.5 exposure assessment. However, the application of LCAQM in spatially extensive exposure modeling is hindered by the difficulty in performing calibration at large spatial scales and the adverse influence of LCAQM residual uncertainty after calibration. We aimed to develop an efficient spatially scalable calibration method for LCAQM and design a residual uncertainty-derived down-weighting strategy to optimize the use of LCAQM data with regulatory monitoring data in PM2.5 modeling. In California, for each monitor from PurpleAir, a global LCAQM network, we identified a station within a 500-m radius from the Air Quality System (AQS), a U.S. regulatory monitoring network. Regional calibration of PurpleAir to AQS was performed at the hourly level with Geographically Weighted Regression (GWR). The calibrated PurpleAir measurements were down-weighted according to their residual uncertainty and then incorporated into a Random Forest (RF) prediction model as a dependent variable to generate 1-km daily PM2.5 exposure estimates. The state-level PurpleAir calibration reduced the systematic bias to ~0 ug/m3 and decreased the random error by 38%. The considerably large samples also enabled quantitative analyses regarding potential factors related to the PurpleAir bias. The RF-based model with both AQS and down-weighted PurpleAir data outperformed the RF model based solely on AQS with an improved CV R2 of 0.86, an improved spatial CV R2 of 0.81, and a lower prediction error of 5.40 ug/m3. The down-weighting allowed the prediction model to show more spatial details of PM2.5 and to better detect pollution hot-spots. Our spatially scalable calibration and down-weighting strategies, for the first time, allowed an effective application of a state-level LCAQM network in high-resolution PM2.5 exposure modeling. The proposed framework can be generalized to regions worldwide for advancing the evaluation of heavy PM2.5 episodes and health-related applications.